While I experimented with several plot types, I ultimately chose a choropleth map, a bar graph, and a line plot. My goal was to use a variety of shapes to convey dynamic movement between the plots, effectively telling the story of the pollutant. I also experimented with displaying the overall AQI and all the pollutants in the AQI as bubbles, but this approach took attention away from the central focus on PM2.5 pollution in Los Angeles.
To create consistency across my visualizations, you’ll notice that each one includes titles, subtitles, and captions. I also minimized the use of axis titles where they weren’t absolutely necessary. In the final infographic, I moved away from traditional titles and subtitles, opting instead for annotations and colors to provide context and guide the reader through the story. Additionally, I used annotations in both the standalone graphs and the final infographic to emphasize key points, such as the 100-ton threshold for major pollution sources in the bar graph.
My general aesthetic preference is quite minimal, so I chose to keep the plot themes simple to allow the bright colors to stand out. This involved removing legends, axis text, axis lines, and background grids, as previously mentioned. Most of these elements weren’t essential for conveying meaning and could be effectively replaced with annotations in the final graphic.
I had a lot of fun experimenting with colors! As mentioned earlier, I chose a photograph of a smoggy downtown Los Angeles, which features distinct layers of smog and sky. Using the color picker in Affinity, I extracted the colors from the image. I then checked them for colorblind accessibility and tested them on a grayscale to ensure they remained distinguishable. To further improve accessibility, I adjusted the saturation and opacity. I’m really happy with the palette I ended up with—it transitions from a cool blue to a bright terracotta, capturing the sky in LA. The cool-to-warm gradient also helps illustrate the progression of pollution levels, from low to high.
I used two fonts throughout my infographic to ensure consistency and readability. Montserrat was reserved for the main title, while Open Sans was used for all other text. Both are sans-serif fonts, chosen for their clarity and modern aesthetic—a subtle contrast to the theme of polluted air. To enhance readability and emphasize important details, I used bold text, often paired with color, to highlight key points in annotations and draw attention to critical statistics.
I designed the infographic to guide the viewer’s eyes in a natural reading flow—moving from left to right and then down, similar to reading a book. However, I was also mindful that not everyone follows the same reading pattern, so I ensured that each graph could stand alone and be understood in any order. That said, I placed the illustration of PM2.5 and its context right at the top, just below the title, as I felt it was crucial for understanding the rest of the infographic.
I spent a lot of time refining the story to ensure it naturally guides the viewer toward key insights while still allowing for personal interpretation. However, I did provide context through the header, the PM2.5 illustration, and concise plot annotations. Instead of over-annotating with additional explanations or takeaways, I used select highlights to guide the reader and let the data speak for itself.
While this project initially started as an exploration without a specific message in mind, a clear takeaway emerged: PM2.5 pollution in Los Angeles is a significant issue, with distinct spatial and temporal patterns. The infographic serves more as an introductory overview (think — PM2.5 Pollution 101) rather than an in-depth analysis, providing viewers with a foundational understanding of the issue.
As mentioned earlier, I created my own color palette based on a photo of smog. Some of the colors were too similar, which could cause accessibility issues for viewers with color blindness or in grayscale. To address this, I adjusted the saturation of certain colors to create more contrast. I also made sure to avoid placing colors that were too similar next to each other, unless they were part of a gradient. Additionally, I added alt text to all of my visuals to further ensure accessibility for all viewers.
I knew I wanted to incorporate a DEI aspect into my choropleth map of PM2.5 pollution in Los Angeles. When I noticed pollution hotspots in Reseda and Central Los Angeles, I decided to check if these areas also ranked high on the CalEnviroScreen 4.0 percentile range. The CalEnviroScreen 4.0 percentile range is a tool used to assess the cumulative environmental, health, and socioeconomic impacts in California. Areas are ranked based on factors like water quality, proximity to hazardous waste sites, and poverty levels. Both Reseda and Central Los Angeles ranked very high, indicating they had high cumulative impacts, including both high PM2.5 levels and significant socioeconomic stressors. To represent this, I initially created a bivariate map. However, I faced challenges trying to explain what the CalEnviroScreen percentile range meant in the context of an infographic. To make this easier to interpret, I simplified the map by focusing solely on PM2.5 pollution and poverty. Despite this, I still found it difficult to display the data in a clear and understandable way, as I would need to explain each of the combinations (high pollution, high poverty; low pollution, high poverty, etc.) within the map for it to make sense to viewers. Since the bivariate map and the pollution distribution map looked very similar, I decided to show just the distribution of PM2.5 pollution in Los Angeles, and note the poverty aspect into the annotations. I think this made it easier to understand, but still hits on the major environmental justice issue at play.
